Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics

Chakrabarti, Chaitali; Luger, George F.

doi:10.1016/j.eswa.2015.04.067

Cited by 65 publications

(34 citation statements)

References 27 publications

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“…In the application of the non-linear ANFIS models, however, a grid partition and sub cluster learning methods were used, whereby the type and number of membership functions (MF) were changed. Several other authors also chose an appropriate structure of non-linear models in a similar manner in their research [36][37][38][39][40][41].…”

Section: Selection Of Appropriate Advanced Machine Learning Algorithmmentioning

confidence: 96%

Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

et al. 2016

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Section: Selection Of Appropriate Advanced Machine Learning Algorithmmentioning

confidence: 96%

Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

et al. 2016

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“…Based on the ontology, the maintenance effort is reduced. A dynamic approach is followed by [14,15]. Their chatbot is capable of creating a dynamic goal fulfillment map to answer requests.…”

Section: Application Domainsmentioning

confidence: 99%

How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace

Wolff

Hobert

Schümann

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Chatbots become quite hyped in recent times as they can provide an intuitive and easy-to-use natural language human-computer interface. Nevertheless, they are not yet widespread in enterprises. Corresponding application areas for collaboration at digital workplaces are lacking and prior research contributions on this topic are limited. In this research paper, we aim at surveying the state of the art as well as showing future research topics. Thus, we conducted a structured literature review and showed that only few first research contributions exist. We also outline current potentials and objectives of chatbot applications. In the discussion of the results of our structured literature review, we show that research gaps are present. To tackle the research gaps, we derive open research questions.

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“…In the next paper, Chakrabarti and Luger (2015) built a chatterbot that can contextualize the conversation and overcome the limitation of utterance-exchange type conversations. This bot is specialized for handling customer service.…”

Section: Related Workmentioning

confidence: 99%

Focused domain contextual AI chatbot framework for resource poor languages

Paul

Latif

Adnan

et al. 2018

Journal of Information and Telecommunication

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In today's business world, providing reliable customer service is equally important as delivering better products for maintaining a sustainable business model. As providing customer service requires human resource and money, businesses are often shifting towards artificial intelligence system for necessary customer interaction. However, these traditional chatbot architectures depend heavily on natural language processing (NLP), it is not feasible to implement for the languages with little to no prior NLP backbone. In this work, we propose a semi-supervised artificially intelligent chatbot framework that can automate parts of primary interaction and customer service. The primary focus of this work is to build a chatbot which can generate contextualized responses in any language without depending much on rich NLP background and a vast number of a prior data set. This system is designed in such a way that with a dictionary of a language and regular customer interaction dataset, it can provide customer services for any business in any language. This architecture has been used to build a customer service bot for an electric shop, and different analysis has been done to evaluate the performance of individual components of the framework to show its competence to provide reliable response generation in comparison with other approaches. ARTICLE HISTORY

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Artificial conversations for customer service chatter bots: Architecture, algorithms, and evaluation metrics

Cited by 65 publications

References 27 publications

Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

Energy efficiency analysis of steam ejector and electric vacuum pump for a turbine condenser air extraction system based on supervised machine learning modelling

How May I Help You? – State of the Art and Open Research Questions for Chatbots at the Digital Workplace

Focused domain contextual AI chatbot framework for resource poor languages

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